Selective rationalization [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]} explains the prediction of complex neural networks by finding a small subset of the input – rationale – that suffices on its own to yield the same outcome as to the original data. To generate high-quality rationales, existing methods often train a cascaded system that consists of two components, i.e., a rationale generator and a predictor. The generator selects a subset of the input explicitly (a.k.a., binarized selection), which is then fed to the predictor. The predictor then predicts the output based only on the subset of features selected by the generator. The rationale generator and the predictor are trained jointly to optimize the prediction performance. Compared to many other interpretable methods [10]}, [11]}, [12]}, [13]} that rely on attention mechanism as a proxy of models' explanation, selective rationalization offers a unique advantage: certification of exclusion, i.e., any unselected input is guaranteed to have no contribution to prediction.
